专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明申请

US20120253930A1 USER INTENT STRENGTH AGGREGATING BY DECAY FACTOR 审中-公开
标题翻译：用衰减因子聚合的用户信度强度
公开(公告)号：US20120253930A1
公开(公告)日：2012-10-04
申请号：US13078300
申请日：2011-04-01
申请人： Bin Gao , Tie-Yan Liu , Jun Yan , Ning Liu , Dou Shen , Ying Li
发明人： Bin Gao , Tie-Yan Liu , Jun Yan , Ning Liu , Dou Shen , Ying Li
IPC分类号： G06Q30/00
CPC分类号： G06Q30/0251
摘要： This application describes a system and method for estimating user intent towards categories of content. The estimation of user intent may be based at least in part on a score for prior user actions and a decay function that is applied to that score to provide an estimate of current user intent. The estimate represents current user intent for time periods in which user actions towards a category of content are negligible or non-existent.
摘要翻译：该应用描述了用于估计用户对内容类别的意图的系统和方法。用户意图的估计可至少部分地基于用于先前用户动作的分数和应用于该分数以提供当前用户意图的估计的衰减函数。估计值表示用户对一类内容的操作可忽略或不存在的时间段的当前用户意图。

2. 发明申请

US20160140254A1 IDENTIFYING AND STRUCTURING RELATED DATA 审中-公开
标题翻译：识别和结构相关数据
公开(公告)号：US20160140254A1
公开(公告)日：2016-05-19
申请号：US14897824
申请日：2013-06-13
申请人： Jun YAN , Ning LIU , Lei JI , Zheng CHEN , MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： Jun Yan , Ning Liu , Lei Ji , Zheng Chen
IPC分类号： G06F17/30 , G06F3/0484
CPC分类号： G06F17/30991 , G06F3/0484 , G06F17/3053 , G06F17/30864 , G06Q30/0629
摘要： Some examples include displaying a user interface that includes attributes and entities that are determined to be related to an input entity identified by a user. Further, some implementations include displaying a structured data table that identifies attribute values associated with the input entity and selected related entities.
摘要翻译：一些示例包括显示包括被确定为与由用户标识的输入实体相关的属性和实体的用户界面。此外，一些实现方式包括显示结构化数据表，其识别与输入实体和所选相关实体相关联的属性值。

3. 发明授权

US08868598B2 Smart user-centric information aggregation 有权
标题翻译：智能用户为中心的信息聚合
公开(公告)号：US08868598B2
公开(公告)日：2014-10-21
申请号：US13586711
申请日：2012-08-15
申请人： Jianwen Zhang , Zhimin Zhang , Jian-Tao Sun , Jun Yan , Ning Liu , Lei Ji , Weizhu Chen , Zheng Chen
发明人： Jianwen Zhang , Zhimin Zhang , Jian-Tao Sun , Jun Yan , Ning Liu , Lei Ji , Weizhu Chen , Zheng Chen
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06F17/30032 , G06F17/30905
摘要： A smart user-centric information aggregation system allows a user to define a region of content displayed in a display of a device and performs information aggregation on behalf of the user. The smart user-centric information aggregation system searches, aggregates and groups information related to content included in the region of content for the user while the user can continue to perform his/her original course of actions without interruption. After finding information related to the desired content, the smart user-centric information aggregation system may notify the user and present the found information to the user upon receiving confirmation from the user. The smart user-centric information aggregation system may continue to find new related information and update the presentation with the newly found information periodically, in some instances without user intervention or input.
摘要翻译：以智能用户为中心的信息聚合系统允许用户定义显示在设备显示器中的内容区域，并代表用户执行信息聚合。智能用户为中心的信息聚合系统在用户可以继续执行他/她的原始行为过程而不间断地搜索，聚合和分组与用户内容区域中包含的内容相关的信息。在找到与期望内容相关的信息之后，智能用户为中心的信息聚合系统可以在接收到来自用户的确认时通知用户并向用户呈现找到的信息。以智能用户为中心的信息聚合系统可以继续寻找新的相关信息，并且在某些情况下，不需要用户干预或输入，定期更新新发现的信息。

4. 发明申请

US20130138655A1 Web Knowledge Extraction for Search Task Simplification 有权
标题翻译： Web知识提取搜索任务简化
公开(公告)号：US20130138655A1
公开(公告)日：2013-05-30
申请号：US13307836
申请日：2011-11-30
申请人： Jun Yan , Lei Ji , Ning Liu , Zheng Chen
发明人： Jun Yan , Lei Ji , Ning Liu , Zheng Chen
IPC分类号： G06F17/30
CPC分类号： G06F17/30702 , G06F17/30867
摘要： Techniques are described for generating structured information from semi-structured web pages, and retrieving the structured knowledge in response to a user query that indicates a query intent. The structured information is automatically extracted offline from semi-structured web pages, through the use of an auto wrapper solution that is noise tolerant, scalable, and automatic. The structured information is stored in a knowledge base, and provided in response to a user search query that indicates a query intent. Extraction of structured information may also include clustering of pages based on their measured similarities. The clusters may be determined based on similar elements in the tag path text data of the pages. A minimum size threshold may be applied to the clusters.
摘要翻译：描述了用于从半结构化网页生成结构化信息的技术，以及响应于指示查询意图的用户查询来检索结构化知识。结构化信息通过使用具有噪声容限，可扩展和自动的自动包装解决方案，从半结构化网页离线自动提取。结构化信息存储在知识库中，并响应于指示查询意图的用户搜索查询而提供。结构化信息的提取还可以包括基于其测量的相似性来聚合页面。可以基于页面的标签路径文本数据中的类似元素来确定簇。可以将最小大小阈值应用于群集。

5. 发明授权

US08290921B2 Identification of similar queries based on overall and partial similarity of time series 有权
标题翻译：基于时间序列的总体和部分相似性识别类似查询
公开(公告)号：US08290921B2
公开(公告)日：2012-10-16
申请号：US11770505
申请日：2007-06-28
申请人： Ning Liu , Jun Yan , Benyu Zhang , Zheng Chen , Jian Wang
发明人： Ning Liu , Jun Yan , Benyu Zhang , Zheng Chen , Jian Wang
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06F17/30864 , G06F17/3064
摘要： Techniques for identifying similar queries based on their overall similarity and partial similarity of time series of frequencies of the queries are provided. To identify queries that are similar to a target query, the query analysis system generates, for each query, an overall similarity score for that query and the target query based on the time series of the query and the target query. The query analysis system also generates, for each query, partial similarity scores for the query and the target query based on various time sub-series of the overall time series of the queries. The query analysis system then identifies queries as being similar to the target query based on the overall similarity scores and the partial similarity scores of the queries.
摘要翻译：提供了基于其查询的时间序列的总体相似性和部分相似性来识别类似查询的技术。为了识别类似于目标查询的查询，查询分析系统根据查询和目标查询的时间序列为每个查询生成该查询和目标查询的总体相似性得分。查询分析系统还根据查询的整个时间序列的各种时间子序列，为每个查询生成查询和目标查询的部分相似度分数。然后，查询分析系统基于查询的总体相似性得分和部分相似性得分将查询识别为与目标查询相似。

6. 发明申请

US20120259801A1 TRANSFER OF LEARNING FOR QUERY CLASSIFICATION 有权
标题翻译：转学习查询分类
公开(公告)号：US20120259801A1
公开(公告)日：2012-10-11
申请号：US13081391
申请日：2011-04-06
申请人： Lei Ji , Jun Yan , Ning Liu , Zheng Chen
发明人： Lei Ji , Jun Yan , Ning Liu , Zheng Chen
IPC分类号： G06F15/18
CPC分类号： G06N99/005
摘要： Transfer of learning trains a new domain for the classification of search queries according to different tasks, as well as the generation of a corresponding domain-specific query classifier that may be used to classify the search queries according to the different tasks in the new domain. The transfer of learning may include preparing a new domain to receive classification knowledge from one or more source domains by populating the new domain with preliminary query patterns extracted for a search engine log. The transfer of learning may further include preparing the classification knowledge in each source domain for transfer to the new domain. The classification knowledge in each source domain may then be transferred to the new domain.
摘要翻译：学习的转移为根据不同任务对搜索查询进行分类的新领域提供了新的领域，以及生成可用于根据新域中的不同任务对搜索查询进行分类的相应的域特定查询分类器。学习的转移可能包括准备一个新的域，以通过用搜索引擎日志提取的初步查询模式填充新域来从一个或多个源域接收分类知识。学习的转移还可以包括准备每个源域中的分类知识以转移到新的域。然后可以将每个源域中的分类知识转移到新域。

7. 发明申请

US20100161596A1 Learning Latent Semantic Space for Ranking 有权
标题翻译：学习潜在语义空间进行排名
公开(公告)号：US20100161596A1
公开(公告)日：2010-06-24
申请号：US12344093
申请日：2008-12-24
申请人： Jun Yan , Ning Liu , Lei Ji , Zheng Chen
发明人： Jun Yan , Ning Liu , Lei Ji , Zheng Chen
IPC分类号： G06F7/06 , G06F17/30
CPC分类号： G06F17/30675
摘要： A tool facilitating learning latent semantics for ranking (LLSR) tailored to the ranking task via leveraging relevance information of query-document pairs to learn a tailored latent semantic space such that other documents are better ranked for the queries in the subspace. The tool applying a learning latent semantics for ranking algorithm integrating LLSR, thereby enabling learning an optimal latent semantic space (LSS) for ranking by utilizing relevance information in the training process of subspace learning. The tool enabling an optimization of the LSS as a closed form solution and facilitating reporting the learned LSS.
摘要翻译：一种通过利用查询文档对的相关性信息来学习定制的潜在语义空间，使其他文档更好地排列在子空间中的查询的方法，帮助学习用于排名任务的潜在语义（LLSR）。该工具应用学习潜在语义用于整合LLSR的排序算法，从而通过在子空间学习的训练过程中利用相关性信息来学习优化潜在语义空间（LSS）进行排名。该工具可以将LSS优化为封闭式解决方案，并有助于报告所学习的LSS。

8. 发明申请

US20090222321A1 PREDICTION OF FUTURE POPULARITY OF QUERY TERMS 审中-公开
标题翻译：预测未来的QUERY条款的普遍性
公开(公告)号：US20090222321A1
公开(公告)日：2009-09-03
申请号：US12147468
申请日：2008-06-26
申请人： Ning Liu , Jun Yan , Zheng Chen , Jian Wang
发明人： Ning Liu , Jun Yan , Zheng Chen , Jian Wang
IPC分类号： G06F17/30
CPC分类号： G06Q30/0202 , G06F16/951 , G06F2216/03
摘要： Disclosed is a system and method that allows a computer system the ability to predict what query terms in a search will be popular. The system creates a unified model that determines the future popularity of a query term over a period of time in the future. The unified model averages the results of three different prediction models to obtain a prediction of the future popularity of a query term. The prediction from the unified model is compared against a threshold value of popularity over a time period. When the predicted popularity of the query exceeds the threshold the term is stored. In some embodiments the period that the term exceeds the threshold may also be stored.
摘要翻译：公开了一种系统和方法，其允许计算机系统预测搜索中的哪些查询术语将是流行的能力。该系统创建一个统一的模型，确定未来一段时间内查询词的未来流行度。统一模型对三种不同预测模型的结果进行平均，以获得对查询词的未来流行度的预测。将统一模型的预测与一段时间内的人气阈值进行比较。当查询的预测流行度超过阈值时，该项被存储。在一些实施例中，术语超过阈值的周期也可以被存储。

9. 发明授权

US07533094B2 Method and system for determining similarity of items based on similarity objects and their features 有权
标题翻译：基于相似对象及其特征确定项目相似度的方法和系统
公开(公告)号：US07533094B2
公开(公告)日：2009-05-12
申请号：US10997749
申请日：2004-11-23
申请人： Benyu Zhang , Hua-Jun Zeng , Wei-Ying Ma , Zheng Chen , Ning Liu , Jun Yan
发明人： Benyu Zhang , Hua-Jun Zeng , Wei-Ying Ma , Zheng Chen , Ning Liu , Jun Yan
IPC分类号： G06F17/30
CPC分类号： G06F17/3069 , Y10S707/99934 , Y10S707/99935 , Y10S707/99936 , Y10S707/99937
摘要： A method and system for determining similarity between items is provided. To calculate similarity scores for pairs of items, the similarity system initializes a similarity score for each pair of objects and each pair of features. The similarity system then iteratively calculates the similarity scores for each pair of objects based on the similar scores of the pairs of features calculated during a previous iteration and calculates the similarity scores for each pair of features based on the similarity scores of the pairs of objects calculated during a previous iteration. The similarity system implements an algorithm that is based on a recursive definition of the similarities between objects and between features. The similarity system continues the iterations of recalculating the similarity scores until the similarity scores converge on a solution.
摘要翻译：提供了一种用于确定项目之间的相似性的方法和系统。为了计算物品对的相似性分数，相似系统初始化每对物体和每对特征的相似性得分。然后，相似系统基于在先前迭代期间计算的特征对的类似得分迭代地计算每对对象的相似性得分，并且基于计算出的对象对的相似性得分来计算每对特征的相似性得分在之前的迭代。相似系统实现了一种基于对象之间和特征之间的相似性的递归定义的算法。相似系统继续重新计算相似性分数的迭代，直到相似性得分收敛于解。

10. 发明申请

US20110289025A1 LEARNING USER INTENT FROM RULE-BASED TRAINING DATA 审中-公开
标题翻译：从基于规则的培训数据学习用户信息
公开(公告)号：US20110289025A1
公开(公告)日：2011-11-24
申请号：US12783457
申请日：2010-05-19
申请人： Jun Yan , Ning Liu , Zheng Chen
发明人： Jun Yan , Ning Liu , Zheng Chen
IPC分类号： G06F15/18 , G06N5/02
CPC分类号： G06N5/025 , G06N20/00
摘要： The search intent co-learning technique described herein learns user search intents from rule-based training data and denoises and debiases this data. The technique generates several sets of biased and noisy training data using different rules. It trains each of a set of classifiers using different training data sets independently. The classifiers are then used to categorize the training data as well as any unlabeled data. The classified data confidently classified by one classifier is added to other training data sets, and the wrongly classified data is filtered out from the training data sets, so as to create an accurate training data set with which to train a classifier to learn a user's intent for submitting a search query string or targeting a user for on-line advertising based on user behavior.
摘要翻译：本文描述的搜索意图共同学习技术从基于规则的训练数据中学习用户搜索意图，并对该数据进行去噪和去噪。该技术使用不同的规则产生几组偏倚和嘈杂的训练数据。它使用不同的训练数据集来独立地训练一组分类器中的每一个。然后，分类器用于对训练数据以及任何未标记的数据进行分类。通过一个分类器自信分类的分类数据被添加到其他训练数据集，并且从训练数据集中过滤出错误分类的数据，以便创建准确的训练数据集，以训练分类器来学习用户的意图用于根据用户行为提交搜索查询字符串或定位用户进行在线广告。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式